352 Prospective Observational Study Comparing Burn Surgeons’ Estimations and Photo-assisted Methods of Skin Graft Healing
Bibliographic record
Abstract
Appropriate graft healing after split-thickness skin graft and early recognition of complications (infection, graft loss, shearing) are critical to burn patient management. Whereas prior retrospective studies have indicated good accuracy of providers’ bedside and photo assessments for graft ratios up to 1.5:1, it remains unclear whether larger graft ratios (up to 4:1), or alternative expansion techniques, such as Meek micrografting, influence the accuracy of graft healing assessments. This study evaluates the concordance of bedside and photograph assessments of graft healing among supervising clinicians at a regional burn center. We evaluated three assessment methods for graft epithelialization: 1) clinicians’ bedside rating, 2) clinician assessment of high definition photographs, and 3) Digital image analysis through color subtraction using Photoshop. We compared each method using a mixed-effects model on absolute agreement using intra-class correlation (ICC) and Bland-Altman (BA) plots. We prospectively enrolled 14 adult burn patients with 38 grafted wounds, to obtain 100 separate assessment sites. Bedside assessments had a mean ICC of 0.62 (compared to digital image analysis) and 0.70 (compared to photo assessment), with confidence intervals of +/- 30% healing on BA plots. Inter-rater reliability of photo assessment was excellent (0.94) among 4 clinicians for average measures. Repeated photo-assisted assessments had good to excellent intra-rater reliability (average ICC 0.88 for high definition photo assessment and 0.97 for digital analysis). Clinicians’ bedside assessments of graft epithelialization had high variability, whereas assessments by photographic techniques had excellent concordance. This study suggests that graft healing assessment can be performed reliably by using high-quality photographs. Clinicians’ judgment for graft epithelialization can be supplemented by use of high quality photographs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".